Two main approaches have been developed for learning first-order planning
(action) models from unstructured data: combinatorial approaches that yield
crisp action schemas from the structure of the state space, and deep learning
approaches that produce action schemas from states represented by images. A
benefit of the former approach is that the learned action schemas are similar
to those that can be written by hand; a benefit of the latter is that the
learned representations (predicates) are grounded on the images, and as a
result, new instances can be given in terms of images. In this work, we develop
a new formulation for learning crisp first-order planning models that are
grounded on parsed images, a step to combine the benefits of the two
approaches. Parsed images are assumed to be given in a simple O2D language
(objects in 2D) that involves a small number of unary and binary predicates
like "left", "above", "shape", etc. After learning, new planning instances can
be given in terms of pairs of parsed images, one for the initial situation and
the other for the goal. Learning and planning experiments are reported for
several domains including Blocks, Sokoban, IPC Grid, and Hanoi.

本研究提出了一种新的学习规划模型的方法，其既有组合方法学习清晰的动作模板，又能够从图像空间中产生动作模板，这是结合前两种方法的优势的一步。

学习基于一阶符号计划的基础表达

Learning First-Order Symbolic Planning Representations That Are Grounded

Subgraph matching is the problem of determining the presence and location(s)
of a given query graph in a large target graph. Despite being an NP-complete
problem, the subgraph matching problem is crucial in domains ranging from
network science and database systems to biochemistry and cognitive science.
However, existing techniques based on combinatorial matching and integer
programming cannot handle matching problems with both large target and query
graphs. Here we propose NeuroMatch, an accurate, efficient, and robust neural
approach to subgraph matching. NeuroMatch decomposes query and target graphs
into small subgraphs and embeds them using graph neural networks. Trained to
capture geometric constraints corresponding to subgraph relations, NeuroMatch
then efficiently performs subgraph matching directly in the embedding space.
Experiments demonstrate NeuroMatch is 100x faster than existing combinatorial
approaches and 18% more accurate than existing approximate subgraph matching
methods.

NeuroMatch 利用图神经网络将查询和目标图分解成小的子图，并在嵌入空间中直接实现子图匹配，实验证明其比现有的组合匹配方法快 100 倍，比现有的近似子图匹配方法精度高 18%。